Optimization of reed-based polyol production driven by response surface methodology and machine learning: Process modeling and hydroxyl value prediction

doi:10.1016/j.indcrop.2024.119752

Optimization of reed-based polyol production driven by response surface methodology and machine learning: Process modeling and hydroxyl value prediction

2024-12-01

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DOI：10.1016/j.indcrop.2024.119752

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In pursuit of sustainable development goals, the transformation of biomass resources into high-value chemicals has become a focal point within the academic community. Despite being a biomass resource, reed (Phragmites australis) has not yet fully realized its potential in the production of bio-based polyols. This study utilized Response Surface Methodology (RSM) and an Artificial Neural Network (ANN) enhanced by a Genetic Algorithm (GA) to model and optimize the liquefaction process of reeds. Furthermore, this study employed a pre-existing predictive model to estimate the hydroxyl value of reed-derived liquefaction products, thereby aiming to curtail expenses during the biomass chemical development phase. The results demonstrated that both the RSM and GA-ANN models accurately predict bio-polyol yield, with the RSM model outperforming the ANN model. Based on optimal conditions predicted by the RSM model, the best experimental process parameters were established: raw material particle size 2-12 mesh, solid-liquid ratio 5:1, glycerol content 32.5%, catalyst content 3.6%, temperature 169 degrees C, and reaction time 58 minutes. Under these conditions, the polyol yield reached 89.145%, with a relative deviation of 2.366%, and the bio-polyol exhibited a viscosity of 0.51 Pa & sdot;s. The predicted hydroxyl value for the bio-based polyol was found to be 399.19 mg KOH/g. The liquefied product is rich in hydroxyl groups, indicating its potential application value in preparing bio-based polymer materials. This study introduces innovative approaches to the economical production of bio-based polyols at the laboratory scale, which may pave the way for their broader industrial implementation.

Keywords:

Biomass Liquefaction

Phragmites australis

Response surface methodology

Artificial neural network

Bio-based Polyols

Optimization of reed-based polyol production driven by response surface methodology and machine learning: Process modeling and hydroxyl value prediction

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